AI Citation Readiness Report
Enter a public domain to check robots.txt, /llms.txt, homepage metadata, JSON-LD schema, and citation readiness.
What AI Index Check Audits
AI Index Check reviews the public signals that help search engines, answer engines, and AI-assisted discovery systems understand a website. The report looks at crawler policy, llms.txt availability, homepage metadata, canonical tags, JSON-LD schema, and whether a page exposes enough crawlable text to be understood without relying on screenshots or private application state.
The goal is practical indexability. A page can be visually polished and still be hard to cite if its entity names are unclear, important claims are hidden in images, schema is invalid, or robots.txt blocks the systems that need to crawl it. The tool turns those checks into a readable readiness report so site owners can prioritize fixes before asking search engines to index more URLs.
Early-stage sites should be especially selective about which URLs they ask search engines to crawl and evaluate. A compact set of strong, self-canonical, useful pages is usually better than a large sitemap filled with thin programmatic pages. AI Index Check is meant to support that kind of disciplined launch process.
Crawlability vs Citation Readiness
Crawlability is the first layer: can a compliant crawler request the page, follow the canonical URL, and read the content without being blocked by policy or network problems? Citation readiness is a broader layer: does the page provide clear, attributable, answerable information that an AI search result could reference with confidence?
These layers overlap but are not the same. A page may be crawlable and still weak for citations if it lacks concise explanations, source links, author or organization clarity, updated facts, or structured data. AI Index Check keeps those concepts separate so teams can avoid treating a single pass/fail signal as a ranking promise.
llms.txt, Robots, Schema, And Metadata
The tool treats llms.txt as an emerging discovery convention. It can describe high value resources, but it does not guarantee that any model, crawler, or answer engine will use the file. Robots.txt remains the public policy surface for compliant crawlers, while schema and metadata help machines identify the page topic, canonical URL, organization, and structured entities.
Strong results usually come from consistent signals: a clear canonical URL, crawlable text, valid JSON-LD, useful headings, and crawler policy that matches the site owner's intent. When these pieces disagree, the recommendations call out the area most likely to confuse indexing or extraction systems.
Who Should Use These Tools
AI Index Check is built for founders, SEO teams, developers, technical marketers, and content operators who need a quick audit before publishing, migrating, or submitting pages for indexing. It is especially useful when a site has added AI-facing files, changed crawler rules, launched new schema, or wants to understand why public pages are not easy to extract.
What This Tool Does Not Guarantee
A good readiness score does not guarantee LLM visibility, search rankings, indexing, training inclusion, or citations. The report is an operational checklist for public technical signals. Use it to reduce avoidable friction, then validate changes in Search Console, server logs, crawler documentation, analytics, and real search result behavior.
Focused SEO Tools
Create a concise llms.txt draft that points AI systems toward your most useful public resources without implying guaranteed visibility.
llms.txt ValidatorCheck whether /llms.txt is fetchable, text-based, structured clearly, and free from common formatting issues.
AI Crawler / robots.txt CheckerInspect robots.txt and summarize whether major AI-related crawlers appear allowed, blocked, or unspecified.
Schema Extractability CheckerExtract homepage metadata and JSON-LD so you can see whether answer engines can understand entity, product, organization, and article signals.
AI Citation Readiness ReportGenerate a score and concrete recommendations across robots.txt, llms.txt, metadata, schema, canonicals, and extractable page content.
AI Crawler Directory
Check crawler-specific robots.txt references before changing policy. The directory covers Googlebot, Google-Extended, GPTBot, ChatGPT-User, OAI-SearchBot, ClaudeBot, PerplexityBot, and other crawler tokens used in AI search, training, retrieval, or control workflows.
Google: Google Search crawling and indexing.
Google-Extended robots.txt checkerGoogle: Control token for certain Gemini and Vertex AI uses outside Google Search.
GPTBot robots.txt checkerOpenAI: OpenAI crawler for content that may be used to train generative AI models.
ChatGPT-User robots.txt checkerOpenAI: User-initiated ChatGPT and Custom GPT requests.
OAI-SearchBot robots.txt checkerOpenAI: OpenAI search discovery for ChatGPT search features.
ClaudeBot robots.txt checkerAnthropic: Anthropic automated web crawler.
PerplexityBot robots.txt checkerPerplexity: Perplexity search and retrieval crawler.
AI Search Guides
Check how AI crawlers use robots.txt, see allow and block examples, and test whether Googlebot, GPTBot, ClaudeBot, PerplexityBot, and other crawlers can access key pages.
Googlebot vs Google-Extended - Robots.txt Rules and AI Search ImpactCompare Googlebot and Google-Extended, see robots.txt examples, and check whether Google crawlers are allowed or blocked.
What Is llms.txt? A Practical Guide for AI SearchLearn what llms.txt is, where it lives, what to include, and why it should be treated as an emerging AI discovery convention.
llms.txt Example for SaaS, Documentation and Tool WebsitesUse a practical llms.txt example for SaaS, documentation, and tool websites, with notes on what to include and avoid.
AI Citation Readiness Checklist - Make Content Easier for AI Tools to ReferenceUse this checklist to make a public page easier for AI search systems to crawl, parse, understand, and responsibly cite.
How to Make a Page Citable by AI Search - Practical GEO ChecklistImprove AI search citability with clearer passages, source clarity, structured data, crawlable content, and canonical consistency.
Sources and Review Policy
AI Index Check verifies crawler roles and AI search guidance against current primary documentation. Technical checks are separated from editorial recommendations, and verification dates are updated only after a manual source review.
Last reviewed: